Generally, driving behavior of a driver is measured using a combination of key performance indices (KPIs), such as number of harsh braking/accelerations/cornering, frequency of tailgating, frequency of speeding above permissible limits, other traffic violations, and the like measured over a period of time. Oftentimes, such KPIs are used by insurance companies to decide auto insurance premiums of the driver. In order to build the driver risk profile, the driving KPIs are aggregated, a risk profile is estimated and insurance premium is computed based on the quantified riskiness of the driver. In such scenarios, driver identification becomes extremely important as the driving behavior is analyzed on a continuous basis and aggregated. When there are multiple passengers inside a vehicle, it is expedient to identify the person who is driving since the driver KPIs need to be associated with the driver who is driving the vehicle and not the passengers.
Driver identification may be done using data obtained from a variety of modalities such as imaging (camera mounted inside the vehicle), directional audio, providing multiple sensors inside the vehicle, such as accelerometer, gyroscope, and magnetometer and vehicle engine related parameters obtained from on-board diagnostic port and the like. However, such identification techniques require infrastructural modifications in the vehicle, a huge installation cost associated with the same, technical compatibility for receiving data from the vehicle's in-built devices, installation of additional communication devices to send real time data to the insurance companies and the like.
Therefore, there is a need for providing efficient solution to the above mentioned problems related to driver identification. There is a need to provide localization of a smartphone of the driver inside a vehicle and association of the same with one or more deterministic characteristics to evaluate driver behavior for driver identification.